Software Algorithms Research Articles

Plasmonic structurally colored surfaces with metal film over microsphere lattices

Structural coloring through plasmonic nanostructures involves the manipulation of light interaction with nanostructured metallic surfaces to selectively reflect specific spectral ranges within the visible spectrum. Unlike conventional pigments that absorb light based on their chemical properties, plasmonic nanostructures achieve color through their physical configuration. This study investigates the potential of generating plasmonic structural colors by utilizing self-assembled colloidal microsphere lattices that are coated with thin metal films. Finite-Difference Time-Domain (FDTD) simulations are employed on realistic models to analyze the optical reflectance of dielectric microsphere lattices ranging in sizes from 300 to 700 nm, coated with varying thicknesses (20–200 nm) of different metals (Al, Ag, Au). The reflectance spectra are then translated into specific colors using a software algorithm, with the color representations plotted on CIE1931 diagrams. Furthermore, to validate the theoretical findings, two-dimensional polystyrene microsphere arrays coated with Au and Ag films are prepared, and their reflectance spectra and color images are examined. The resulting plasmonic colors can be adjusted by altering the sphere size, the type of metal used, and the thickness of the coating, demonstrating their potential for diverse color-based applications.

Determining the binding mechanism of B12N12(Zn) with CH4, CO, CO2, H2O, N2, NH3, NO, NO2, O2, and SO2 gases

In this study, an exploration of molecular interactions between CH4, CO, CO2, H2O, N2, NH3, NO, NO2, O2, SO2 gas molecules and B12N12(Zn) nanocage is conducted using advanced computational techniques, ωB97XD/Def2tzvp, unraveling fundamental behaviors. Employing global optimization methods and sophisticated tools like the bee colony algorithm in ABCluster software, the research offers insights into energy adsorption processes, confirming molecular stability through DFT calculations. The determination of electrophilicity index values through conceptual DFT analysis sheds light on relative reactivity levels and charge transfer phenomena, emphasizing that in some cases the nanocage's role as a potential electron acceptor. Natural bond analysis of charge transfer direction and valence shell orbital interactions enriches understanding, supported by comprehensive parameter compilation and critical point visualization. Further confirmation of interaction types and strengths through G(r)/V(r) ratios and ELF values enhances comprehension through quantum theory of atoms in molecule analysis. Ultimately, this study contributes significantly to computational chemistry, laying foundations for molecular design and engineering advancements. It sets the stage for future progress in materials science and catalysis, promising innovation in sustainable energy solutions and technological development.

Identification of key genes associated with cervical cancer based on bioinformatics analysis

BackgroundCervical cancer has extremely high morbidity and mortality, and its pathogenesis is still in the exploratory stage. This study aimed to screen and identify differentially expressed genes (DEGs) related to cervical cancer through bioinformatics analysis.MethodsGSE63514 and GSE67522 were selected from the GEO database to screen DEGs. Then GO and KEGG analysis were performed on DEGs. PPI network of DEGs was constructed through STRING website, and the hub genes were found through 12 algorithms of Cytoscape software. Meanwhile, GSE30656 was selected from the GEO database to screen DEMs. Target genes of DEMs were screened through TagetScan, miRTarBase and miRDB. Next, the hub genes screened from DEGs were merged with the target genes screened from DEMs. Finally, ROC curve and nomogram analysis were performed to assess the predictive capabilities of the hub genes. The expression of these hub genes were verified through TCGA, GEPIA, qRT-PCR, and immunohistochemistry.ResultsSix hub genes, TOP2A, AURKA, CCNA2, IVL, KRT1, and IGFBP5, were mined through the protein-protein interaction network. The expression of these hub genes were verified through TCGA, GEPIA, qRT-PCR, and immunohistochemistry, and it was found that TOP2A, AURKA as well as CCNA2 were overexpressed and IGFBP5 was low expression in cervical cancer.ConclusionsThis study showed that TOP2A, AURKA, CCNA2 and IGFBP5 screened through bioinformatics analysis were significantly differentially expressed in cervical cancer samples compared with normal samples, which might be biomarkers of cervical cancer.

Automatic Defect Detection Instrument for Spherical Surfaces of Optical Elements

In order to realize automatic surface defect detection for large aperture precision spherical optical elements, an automatic surface defect detection instrument has been designed. The instrument consists of a microscopic imaging system, illumination system, motion scanning system, and a software algorithm system. Firstly, a multi-angle channel illumination source and a coaxial illumination source were designed. Bright and dark field images of surface defects were captured by cooperating with an automatic zoom microscope. Then, algorithms for scanning trajectory planning, image stitching, and intelligent defect recognition were designed to achieve full-aperture surface image acquisition and defect quantification detection. The automated defect detection process of the instrument is summarized and introduced. Finally, the experimental platform was constructed, which can work well for the optical elements with a maximum diameter of 400 mm and a relative aperture R/D value of 1. It takes about 15 min to detect an optical element with a diameter of 200 mm in dark-field imaging mode. As a result, the minimum line width of scratch detectable is 2 μm and the minimum diameter of pitting detectable is 4 μm. Clearly, the instrument can realize the automatic detection of surface defects of spherical optical elements, and has the advantages of a high efficiency, stability, reliability, quantification, and data traceability.

Creation of a prototype simulator object for quality control of bone segmentation algorithms for use in tomographic post-processing software

Considering the growing influence of Artificial Intelligence (AI) in medicine, especially in radiology, and the advances in computational power and theoretical understanding of AI algorithms (MESKÓ; GÖRÖG, 2020), concerns about the reliability of these methods arise. This study developed a prototype simulator object for quality control of bone segmentation algorithms in computed tomography. To achieve this objective, a 3D simulator object was created using advanced printing techniques, with materials that faithfully replicate the density and hardness of human bones, such as RadioMatrix. The results indicated that bone segmentation in post-processing software is strongly influenced by tissue density and that currently available AI models lack explainability (EXAI). The analysis revealed the need for algorithms that consider not only density but also the specific shapes and characteristics of bones. It is concluded that the developed prototype is effective in the evaluation and quality control of bone segmentation algorithms, promoting greater safety in clinical application and better explainability of bone segmentation algorithms in post-processing software for computed tomography.

A novel hybrid method integrating multi-objective optimization with emergy analysis for building renewal strategy

Building renewal has become a promising approach for improving energy efficiency and reaching carbon neutrality targets. Two renewal strategies, namely, the retrofitting of existing buildings and their demolition and reconstruction of a new one, are often explored and compared to determine which strategy is the optimal one. However, the decision is still uncertain due to the large domain space of strategies and the lack of comprehensive methods. This paper, therefore, presents a novel hybrid method to help decision-makers identify the optimal building renewal strategy based on the integration of multi-objective optimization algorithm with emergy analysis. A simulation-based non-dominated sorting genetic algorithm is developed to select the optimal retrofitting strategy, where dynamic simulation model is created in EnergyPlus software and non-dominated sorting genetic algorithm is written in Python. Then, the equivalent reconstruction strategy is projected by the optimal retrofitting strategy while meeting mandatory standards. A comparison between the optimal retrofitting strategy and the equivalent reconstruction strategy can be made by applying emergy analysis considering the whole life cycle phase. A fictional office building was chosen as a case study to illustrate the practical implementation of the proposed method. High-cost building retrofitting strategies were found to consistently yield the highest energy savings and thermal comfort, and the energy use decreased by 4.63 % to 48.25 %. The input emergy of the original building, retrofitting building and reconstruction building are 3.16E + 20 sej, 1.41E + 21 sej, and 1.46E + 21 sej, respectively, and the output emergy are 2.39E + 20 sej, 1.66E + 20 sej, and 4.76E + 19 sej. The results also indicate that the sustainability of the retrofitting strategy is better than that of the reconstruction strategy. This paper provides a guidance method and framework for decision-makers and policy-makers to identify the optimal building renewal strategy for reducing the energy use and achieving carbon peak targets.

Optical, physical, mechanical, structural, and radiation shielding investigations of B2O3–TeO2-GeO2-MgO-PbO for ionizing protection and optical transmission application

This study aims to investigate a new heavy glass system's potential use in the radiation shielding and optical transmission applications. Glass former like borate is replaced in this work by heavy metal oxides like lead oxide in the following glass system: (35-x) B2O3–20TeO2-10GeO2-35MgO-xPbO (where x = 2.5, 5, 7.5, and 10 mol%). Through the melt-annealing process, four transparent glasses were synthesized. A number of physical properties were reported, namely the boron–boron separation x 10−8 m, field strength x 1018 (cm2), inter nuclear distance x 10−8 m, ion concentration (N) x 1023 (ion cm3), molar volume (cm3/mol), molecular weight (g/mol), and polaron radius x 10−9 m. Moreover, the following mechanical and structural properties of the synthesized glasses were explored: bulk modulus (B, GPa), dissociation energy (kcal/cm3), fractal bond connectivity (d), longitudinal modulus (L, GPa), hardness (H, GPa), oxygen packing density, packing density (Vt), oxygen molar volume, packing factor (Vi), shear modulus (S, GPa), Poisson radius (σ), and Young modulus (Y, GPa). To study the bulk glasses' nature, the X-ray diffraction was examined. Optical absorption along with optical transmission profile have been recorded to extract various optical properties, as well as the direct and indirect band gap. The transmittance spectra of the prepared glass samples' FTIR were investigated within the 400–2000 cm−1 range. This allowed for the examination of molecular structural groups and vibrational modes. Radiation shielding parameters have been calculated and studied using XCOM software and MCNP-5 algorithm.

Identification of Potential Biomarkers for Patients with DWI-Negative Ischemic Stroke

Ischemic stroke is the leading cause of long-term disability in adults, accounting for 80% of stroke cases. Diffusion weighted imaging (DWI) examination is the main test for acute ischemic stroke, but in recent years, several studies have shown that some patients show negative DWI examination after the onset of ischemic stroke with symptoms of significant neurological deficits. In this study, we investigated potential biomarkers related to immune metabolism in the peripheral blood of DWI-negative versus DWI-positive patients after ischemic stroke and explored their possible regulatory processes in ischemic stroke. The datasets related to ischemic stroke were downloaded from the GEO database, immune-related genes and metabolism-related genes were obtained from the ImmPort database and MSigDB database, respectively, and immune-related differential genes were obtained based on immune scores using the algorithm of the R software package “GSVA.” Candidate genes were selected based on intersections, hub genes were screened using the algorithm in Cytoscape software, and finally, GeneMANIA analysis, GSEA enrichment analysis, subcellular localization, gene transcription factor and gene-drug interaction networks, and disease correlation analyses were performed for the hub genes. Five hub genes (GART, TYMS, PPAT, CTPS1, and PAICS) were obtained by PPI network analysis and software analysis. Among them, PPAT and PAICS may be the real hub genes with consistent and significantly differentiated results from the discovery and validation sets. The functions of these hub genes may be related to pathways such as nucleotide biosynthetic processes. The constructed hub gene ceRNA network showed that hsa-10a-5p is the key miRNA connecting PAICS and multiple lncRNAs in this study. Differential genes related to immunity and metabolism in DWI-negative and DWI-positive patients after IS were identified using bioinformatics analysis, and their pathways and related TF-RNAs, miRNAs, and lncRNAs were identified. These genes may be considered effective targets for the diagnosis and treatment of ischemic stroke.

Investigation of Chemiresisitive Electronic Nose for the Analysis of Multiple Analytes Using Pattern Recognition Algorithm

The multiple analytes produced during the operation of nuclear facilities are required to monitor the smooth operation of the plant in the environment of high temperature and radioactivity in real time. A chemiresisitive electronic nose was investigated and developed to analyze the multiple analytes generated in the nuclear reactor/allied facilities. An electronic nose consists of chemiresisitive sensor, array, housing, hardware, software, and pattern recognition algorithm. The sensor and array of different semiconductor metal oxides were prepared, processed, and developed to sense the multiple analytes. The hardware and data acquisition software (DAS) was designed and developed to acquire the dynamic responses from the array of four sensors. The hardware provides a low excitation voltage for measurement of the dynamic response of four sensors towards the improvement of the life of the sensor. The various experiments were conducted with multiple analytes at different temperatures to study the analysis of analytes. The performance of the hardware and DAS were tested and evaluated with the sensor array responses towards three analytes, viz., hydrogen (H2), formaldehyde (HCHO), and hydrazine (NH2NH2). Different features evaluated from the response traces were processed to teach the instrument using pattern recognition algorithms. The training and real-time testing of the sensor array realized the qualitative discrimination and quantitative estimation of the analytes.

Automated design of embedded digital signal processing systems on SOC platform

The object of the study is the procedures for automated design and analysis of digital signal processing algorithms on the SoC technology platform. The subject of the study is models, methods and procedures for designing and optimal selection of SoC components for the implementation of digital signal processing algorithms for audio spectrum. The aim of the study is to develop models and procedures for determining the possibilities of a compromise distribution of signal processing algorithm computations in the cycle of computer-aided design on the SoC technology platform in terms of performance and the feasibility of using hardware and software algorithms realization. The article solves the following tasks: consideration of the procedures for interacting the processor core with programmable logic as part of system-on-chip systems; development of procedures for computer-aided design and analysis of signal processing systems using programming languages and hardware description languages for the implementation of embedded systems. The following methods are being used: implementation of digital signal processing algorithms in the C programming language and high-level synthesis tools for realizing IP blocks, diagnostic experiment by generating test signal patterns, and analysis of the processing results at the system output. The results achieved. Based on the analysis of the procedures for the interaction of the processor core and programmable logic on the selected SoC platform, a model of the audio spectrum signal processing system is designed. The practical implementation was performed based on the Vivado/Vitis/Vitis HLS CAD tool stack. The proposed model was verified using a programmable test signal generator and analyzing the obtained characteristics of digital filters at the system output. Conclusions. The article analyzes the principles of designing embedded information processing systems implemented in system-on-chip. The principles of building and analyzing digital signal processing systems based on system-on-chip containing programmable logic and processor parts are considered. The developed methods have been tested on the algorithms of CIC and FIR filters on the technological platform of SoC FPGA of the ZYNQ-7000 family of Xilinx company.

Assessing the impact of nodule features and software algorithm on pulmonary nodule measurement uncertainty for nodules sized 20 mm or less.

Measurements are not exact, so that if a measurement is repeated, one would get a different value each time. The spread of these values is the measurement uncertainty. Understanding measurement uncertainty of pulmonary nodules is important for proper interpretation of size and growth measurements. Larger amounts of measurement uncertainty may require longer follow-up intervals to be confident that any observed growth is due to actual growth rather than measurement uncertainty. We examined the influence of nodule features and software algorithm on measurement uncertainty of small, solid pulmonary nodules. Volumes of 107 nodules were measured on 4-6 repeated computed tomography (CT) scans (Siemens Somatom AS, 100 kVp, 120 mA, 1.0 mm slice thickness reconstruction) prospectively obtained during CT-guided fine needle aspiration biopsy between 2015-2021 at Department of Diagnostic, Molecular, and Interventional Radiology in Icahn School of Medicine at Mount Sinai, using two different automated volumetric algorithms. For each, the coefficient of variation (standard deviation divided by the mean) of nodule volume measurements was determined. The following features were considered: diameter, location, vessel and pleural attachments, nodule surface area, and extent of the nodule in the three acquisition dimensions of the scanner. Median volume of 107 nodules was 515.23 and 535.53 mm3 for algorithm #1 and #2, respectively with excellent agreement (intraclass correlation coefficient =0.98). Median coefficient of variation of nodule volume was low for the two algorithms, but significantly different (4.6% vs. 8.7%, P<0.001). Both algorithms had a trend of decreasing coefficient of variation of nodule volume with increasing nodule diameter, though only significant for algorithm #2. Coefficient of variation of nodule volume was significantly associated with nodule volume (P=0.02), attachment to blood vessels (P=0.02), and nodule surface area (P=0.001) for algorithm #2 using a multiple linear regression model. Correlation between the coefficient of variation (CoV) of nodule volume and the CoV of the x, y, z measurements for algorithm #1 were 0.29 (P=0.0021), 0.25 (P=0.009), and 0.80 (P<0.001) respectively, and for algorithm #2, 0.46 (P<0.001), 0.52 (P<0.001), and 0.58 (P<0.001), respectively. Even in the best-case scenario represented in this study, using the same measurement algorithm, scanner, and scanning protocol, considerable measurement uncertainty exists in nodule volume measurement for nodules sized 20 mm or less. We found that measurement uncertainty was affected by interactions between nodule volume, algorithm, and shape complexity.

Abnormal genes and pathways that drive muscle contracture from brachial plexus injuries: Towards machine learning approach

In order to clarify the pathways closely linked to denervated muscle contracture, this work uses IoMT-enabled healthcare stratergies to examine changes in gene expression patterns inside atrophic muscles following brachial plexus damage. The gene expression Omnibus (GEO) database searching was used to locate the dataset GSE137606, which is connected to brachial plexus injuries. Strict criteria (|logFC|≥2 & adj.p < 0.05) were used to extract differentially expressed genes (DEGs). To identify dysregulated activities and pathways in denervated muscles, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were used. Hub genes were found using Cytoscape software's algorithms, which took into account parameters like as proximity, degree, and MNC. Their expression, enriched pathways, and correlations were then examined. The results showed that 316 DEGs were predominantly concentrated in muscle-related processes such as tissue formation and contraction pathways. Of these, 297 DEGs were highly expressed in denervated muscles, whereas 19 DEGs were weakly expressed. GSEA showed improvements in the contraction of striated and skeletal muscles. In addition, it was shown that in denervated muscles, Myod1, Myog, Myh7, Myl2, Tnnt2, and Tnni1 were elevated hub genes with enriched pathways such adrenergic signaling and tight junction. These results point to possible therapeutic targets for denervated muscular contracture, including Myod1, Myog, Myh7, Myl2, Tnnt2, and Tnni1. This highlights treatment options for this ailment which enhances the mental state of patient.

Structural Synthesis of Platform Type Manipulators via Connection Based Adjacency Matrix: Topology Generation

Abstract This paper focuses on the design procedures of Alizade's direct and Gezgin's inverse structural synthesis methodologies for platform type manipulators. These task based synthesis procedures includes versatile methodologies for any designer to generate various platform type manipulators with respect to given constraints via simple universal mobility formulation. However they rely on manual generation of mobility independent coarse topological structures, where it is nearly impossible to generate all possible results. In light of this, current study introduces a unique connection based adjacency matrix (CAM) for topology representation, in which legs connected to the fixed ground and platform connections (hinges and branches) are treated separately. The main objective of CAM is to ensure an efficient transition from manual topology generation to automated methods by providing compatibility with software algorithms to describe topologies. Throughout the paper CAM based automated topology generation algorithm that reveals all possible unique coarse topologies with respect to given design constraints is also presented along with examples. Introduced algorithm both allows investigation of isomorphism and eliminates necessity of inversion.

A Bayesian approach to analyzing long-term agricultural experiments

Effective and flexible statistical analyses are key to getting the most out of long-term experiments (LTEs). Here, we aim to introduce Bayesian analysis to the wider LTE community and show how the modelling process differs from traditional statistical analyses. Bayesian methods have become increasingly popular due to more flexibility in model development with better access to statistical software and sampling algorithms. Using Bayes' Theorem, model coefficients are estimated by incorporating any prior knowledge we may have on model terms. Including prior knowledge in this way requires a different estimating procedure for a fitted model. Bayesian model coefficients are usually sampled from thousands of samples from one or more runs of a Markov Chain. We present the use of Bayesian analyses through three examples. Example 1 illustrates a single regression with and without factors using the Broadbalk Long-Term Experiment, showing how the estimated model changes with more uncertainty in our prior knowledge of model coefficients. Example 2 demonstrates the use of multiple regression, predicting grain yield from factor variables and seasonal weather variables. Example 3 shows an estimation of soil carbon changes under crop rotation and fertilization treatments with a hierarchical time series model using a Swedish soil fertility experiment.

METHODS OF REPRESENTATION OF 3D OBJECTS FOR LEARNING GENERATIVE NEURAL NETWORKS

The paper considered various methods of three-dimensional objects generating by using of neural networks. Several key elements of the methodologies of this type of synthesis of new information were singled out, on the basis of which a new way of three-dimensional objects representing was proposed for its use for typical generative models of neural networks training. The purpose of the work is to develop a method of representing of three-dimensional objects that would satisfy the criterion of high density of information useful for a generative model. The minimization of the redundancy of the information generated together with the minimization of the losses associated with the process of transition from a three-dimensional way of representing of an object to a two-dimensional one (with which the existing generative models can cope quite well) are the key aspects of the proposed method of representation. The methodology for solving of the problem given consists in building of a mathematical model of a new type of representation of three-dimensional objects; development of a software algorithm that implements a mathematical model; and testing this representation based on a typical generative neural network model. The scientific novelty is that for the first time such a type of representation of a three-dimensional object was proposed, which could be used for typical generative models training. Conclusions. The proposed method of representing of a three-dimensional object showed its viability even in the context of training of a small typical generative model DCGAN. Prospects for further research of the proposed method for training of other typical generative models were also determined, because this method could be quite easily adapted to representations of input and output data of a wide range of neural network architectures.

An Examination of the Use of Artificial Intelligence in Orthopaedic Surgery

Artificial intelligence (AI) is being used more and more in numerous fields, and the medical industry is no exception. AI is demonstrating potential as a helpful tool in all facets of patient care pathways, including research in healthcare. Due to the practically exponential expansion in computer processing power, cloud computing, and the invention and improvement of software algorithms specifically designed for medical tasks, artificial intelligence (AI) systems are becoming more and more significant in the fields of medicine and orthopaedic surgery. Machine-based integration of imaging studies is particularly ripe for the field of orthopaedic disorders because of the extensive role of technologies like medical imaging that bring high sensitivity, specificity, and positive/negative prognostic value to the management of orthopaedic disorders, among other applications. In orthopaedic surgery, practical applications include real-time rehabilitation monitoring and surgical training; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and diagnostics, such as fracture recognition and tumor detection. This study aims to outline current clinical uses of AI in orthopaedic surgery and to provide a thorough grasp of AI and its subfields.

Comparison of range of motion between 2-year clinical outcomes and predictions of a static scapula preoperative planning software for reverse shoulder arthroplasty

BackgroundPreoperative planning has gained popularity in the management of reverse shoulder arthroplasty (RSA). Commercially available software provides 3-dimensional segmentation of scapula and humerus, as well as providing arc of motion for the implanted articulation and identifying potential areas of bony impingement. However, these software algorithms use a fixed scapula model, disregarding the preoperative clinical range of motion (C-ROM) of the patient, be it glenohumeral or scapulothoracic, as well as any soft tissue parameters. This study aims to compare the ROM based on preoperative planning software by using the implant position from postoperative computed tomography (CT) images (predicted ROM using preoperative planning software [P-ROM]), with the C-ROM assessed at minimum of 2 years of follow-up. MethodsPreoperative and postoperative CT scans of 46 patients who underwent primary RSA between 2017 and 2021 were analyzed. At the postoperative 2-year review, each patient was assessed for active ROM. Implant size and position based on operative notes and postoperative CT scans were used to replicate the performed surgery in the planning software. Abduction, flexion, and external rotation motion were simulated and recorded. The relationship between C-ROM and P-ROM was investigated using linear regression analysis, Pearson correlation coefficient, and paired t-test. ResultsP-ROM was significantly lower than C-ROM at 2 years postoperatively (P < .001), with an average discrepancy of 78° in abduction, 47° in flexion, and 37° in external rotation (C-ROM: abduction 155° ± 21° [80°-180°]; flexion 160° ± 17° [90°-180°]; external rotation 52° ± 14° [10°-80°] vs. P-ROM: abduction 77° ± 13° [53°-107°]; flexion 112° ± 25° [67°-180°]; external rotation 15° ± 21° [0°-79°]). The linear regression analysis indicated weak agreement between C-ROM and P-ROM (abduction R2 = 0.03; flexion R2 = 0.01; external rotation R2 = 0.04). Pearson’s correlation coefficients revealed weak correlations of −0.18, 0.03, and 0.21 for abduction, flexion, and external rotation, respectively. ConclusionP-ROM based on preoperative software in its current form does not allow the prediction of the C-ROM at 2 years of follow-up for patients undergoing RSA.

Prevalence of atrial fibrillation on a 24-hour Holter in adult Indians

ObjectiveTo evaluate paroxysmal atrial fibrillation (AF) prevalence in Indian adults who completed 24-Hour Holter monitoring. MethodsA total of 23,847 patients (36.9 % women) were analyzed for AF duration using a software algorithm. ResultsAF was diagnosed in 4153 (17.4 %) patients with a median AF duration of 13 min and 55 s. ConclusionAF prevalence was high and largely untreated. The short duration of AF episodes indicates a low likelihood of detection during clinical visits, highlighting its potential underestimation in Indian healthcare.

39‐3: Invited Paper: Kirameki Display: Technical Approaches to Represent Real Texture with Light Fields

We have developed a “Kirameki display” that can represent a real texture of materials. (“Kirameki” means a shining/glittering sense in Japanese.) In addition, we have investigated an improvement of “texture representation” by optimization of both an optical design and a software algorithm. Lastly, we discuss the future technical fields for texture representation on displays.

Accuracy of Complete-Arch Scans Obtained by Intraoral Scanner and Smartphone Three-Dimensional Scanning Applications With Different Smartphone Position Setups: An In Vitro Study.

The high cost of intraoral scanners (IOS) for complete-arch scans makes them less accessible for many dental practitioners. As a viable alternative, smartphone scanner applications (SMP) provide comparable scanning capabilities at a significantly low cost. However, there is limited data on the accuracy of SMP, especially when used in various smartphone positions. This study aimed to compare the three-dimensional (3D) and linear accuracy of complete-arch scans acquired by an IOSand SMP (KIRI Engine, KIRI Innovations, Guangdong, China) at three shooting angles (0°, 45°, and 90° for SMP_3A) and two shooting angles (30° and 60° for SMP_2A). A stone dental cast was scanned with a laboratory scanner as a reference, with 11 scans performed by an IOS, SMP_2A, and SMP_3A. In 3D analysis, trueness and precision were evaluated through superimposition with the reference scanand within each group, respectively, using the best-fit algorithm of Geomagic Wrap software (3D Systems, Inc., Rock Hill, SC). Trueness in linear discrepancy was assessed by comparing the occlusal-cervical and mesiodistal dimensions of reference teeth (canine, premolar, and molar), intercanine width, and intermolar width on the digital casts to measurements of the stone cast, while precision was measured using the coefficient of variance. Differences between groups were analyzed using the Friedman test, followed by the Dunn-Bonferroni post hoc test with a significance level set at 0.05. IOS exhibited significantly lower trueness than SMP_2A (p = 0.003) with significantly greater width discrepancies on canines (p = 0.001) and molars (p < 0.001). Discrepancy patterns differed among the three scanning methods. The IOS showedgreater discrepancieson the occlusal surfaces of posterior teeth. While SMP_3A demonstrated higher variation on the palatal surfaces and interproximal areas of posterior teeth. For precision,SMP_3A (p = 0.028) and SMP_2A (p = 0.003) showed a significantly lower precision in 3D analysis, but a comparable reproducibility in linear measurement to IOS. TRIOS IOS (3Shape,Copenhagen, Denmark) exhibited lower trueness in 3D and linear accuracy analyses for complete-arch scans. The positions of the smartphonesignificantly enhanced trueness at the undercut region. SMP_2A and SMP_3A can be a potential alternative for precise linear measurement in complete-arch scans with selective use.